Technical Review 1037456: Measure and optimize model performance with ROC and AUC

lootle1 · lootle1 · commit aa1f3badbfcb · 2025-04-03T10:23:04.000-05:00
diff --git a/learn-pr/azure/optimize-model-performance-roc-auc/1-introduction.yml b/learn-pr/azure/optimize-model-performance-roc-auc/1-introduction.yml
@@ -1,13 +1,13 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.introduction
-title: Introduction
-metadata:
-  title: Introduction
-  description: Introduction to the ROC AUC module.
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 2
-content: |
-  [!include[](includes/1-introduction.md)] 
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.introduction
+title: Introduction
+metadata:
+  title: Introduction
+  description: Introduction to the ROC AUC module.
+  ms.date: 04/03/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 2
+content: |
+  [!include[](includes/1-introduction.md)] 
diff --git a/learn-pr/azure/optimize-model-performance-roc-auc/2-receiver-operator-characteristic-curve.yml b/learn-pr/azure/optimize-model-performance-roc-auc/2-receiver-operator-characteristic-curve.yml
@@ -1,13 +1,13 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.receiver-operator-characteristic-curve
-title: Analyze classification with receiver operator characteristic curves
-metadata:
-  title: Analyze classification with receiver operator characteristic curves
-  description: Conceptual unit introducing ROC curves in machine learning
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 4
-content: |
-  [!include[](includes/2-receiver-operator-characteristic-curve.md)]
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.receiver-operator-characteristic-curve
+title: Analyze classification with receiver operator characteristic curves
+metadata:
+  title: Analyze Classification with Receiver Operator Characteristic Curves
+  description: Conceptual unit introducing ROC curves in machine learning
+  ms.date: 04/03/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 4
+content: |
+  [!include[](includes/2-receiver-operator-characteristic-curve.md)]
diff --git a/learn-pr/azure/optimize-model-performance-roc-auc/3-exercise-evaluate-roc-curves.yml b/learn-pr/azure/optimize-model-performance-roc-auc/3-exercise-evaluate-roc-curves.yml
@@ -1,14 +1,14 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.exercise-evaluate-roc-curves
-title: Exercise - Evaluate ROC curves
-metadata:
-  title: Exercise - Evaluate ROC curves
-  description: Exercise about good and bad ROC curves in machine learning
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 8
-sandbox: true
-notebook: notebooks/9-3-evaluate-roc-curves.ipynb
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.exercise-evaluate-roc-curves
+title: Exercise - Evaluate ROC curves
+metadata:
+  title: Exercise - Evaluate ROC Curves
+  description: Exercise about good and bad ROC curves in machine learning
+  ms.date: 04/03/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 8
+sandbox: true
+notebook: notebooks/9-3-evaluate-roc-curves.ipynb
+
diff --git a/learn-pr/azure/optimize-model-performance-roc-auc/4-compare-optimize-curves.yml b/learn-pr/azure/optimize-model-performance-roc-auc/4-compare-optimize-curves.yml
@@ -1,14 +1,14 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.comparing-optimizing-curves
-title: Compare and optimize ROC curves
-metadata:
-  title: Compare and optimize ROC curves
-  description: Conceptual unit about comparing and optimizing machine learning models ROC curves
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 4
-content: |
-  [!include[](includes/4-compare-optimize-curves.md)]
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.comparing-optimizing-curves
+title: Compare and optimize ROC curves
+metadata:
+  title: Compare and Optimize ROC Curves
+  description: Conceptual unit about comparing and optimizing machine learning models ROC curves
+  ms.date: 04/03/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 4
+content: |
+  [!include[](includes/4-compare-optimize-curves.md)]
+
diff --git a/learn-pr/azure/optimize-model-performance-roc-auc/5-tune-auc-curves.yml b/learn-pr/azure/optimize-model-performance-roc-auc/5-tune-auc-curves.yml
@@ -1,14 +1,14 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.exercise-tune-auc-curves
-title: Exercise - Tune the area under the curve
-metadata:
-  title: Exercise - Tune the area under the curve
-  description: Exercise unit about tuning under the curve in machine learning
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 12
-sandbox: true
-notebook: notebooks/9-5-tune-auc-curves.ipynb
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.exercise-tune-auc-curves
+title: Exercise - Tune the area under the curve
+metadata:
+  title: Exercise - Tune the Area Under the Curve
+  description: Exercise unit about tuning under the curve in machine learning
+  ms.date: 04/03/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 12
+sandbox: true
+notebook: notebooks/9-5-tune-auc-curves.ipynb
+
diff --git a/learn-pr/azure/optimize-model-performance-roc-auc/8-knowledge-check.yml b/learn-pr/azure/optimize-model-performance-roc-auc/8-knowledge-check.yml
@@ -1,48 +1,48 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.knowledge-check
-title: Module assessment
-metadata:
-  title: Module assessment
-  description: Multiple-choice questions
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 3
-quiz:
-  title: Check your knowledge
-  questions:
-  - content: 'What do TPR and FPR mean?'
-    choices:
-    - content: "TPR is the number of correct responses. FPR is the number of incorrect responses."
-      isCorrect: false
-      explanation: "Incorrect."
-    - content: "TPR is the proportion of answers that were provided correctly as 'true'. FPR is the proportion of answers that were provided incorrectly as 'true'."
-      isCorrect: true
-      explanation: "Correct."
-    - content: "TPR is the proportion of answers that were provided correctly as 'true'. FPR is the proportion of answers that were provided incorrectly as 'false'."
-      isCorrect: false
-      explanation: "Incorrect."
-  - content: 'What are on the X and Y axes in an ROC plot?'
-    choices:
-    - content: 'X-axis: FP rate, Y-axis: TP rate'
-      isCorrect: true
-      explanation: "Correct."
-    - content: "X-axis: Number of FPs, Y-axis: Number of TPs"
-      isCorrect: false
-      explanation: "Incorrect. "
-    - content: "X-axis: Number of TPs, Y-axis: Number of FPs"
-      isCorrect: false
-      explanation: "Incorrect."
-  - content: 'What does area under the curve for an ROC plot tell us?'
-    choices:
-    - content: "How well the model works at its optimum decision threshold"
-      isCorrect: false
-      explanation: "Incorrect. This information might be obtainable from the ROC plot but we can't get this information from the AUC."
-    - content: "Which is the optimum decision threshold?"
-      isCorrect: false
-      explanation: "Incorrect. AUC is a summary metric that is too simplified to provide this information."
-    - content: "It gives a summary of how well a model works across various thresholds."
-      isCorrect: true
-      explanation: "Correct."
-
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.knowledge-check
+title: Module Assessment
+metadata:
+  title: Module Assessment
+  description: Multiple-choice questions
+  ms.date: 04/03/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 3
+quiz:
+  title: Check your knowledge
+  questions:
+  - content: 'What do TPR and FPR mean?'
+    choices:
+    - content: "TPR is the number of correct responses. FPR is the number of incorrect responses."
+      isCorrect: false
+      explanation: "Incorrect."
+    - content: "TPR is the proportion of answers that were provided correctly as 'true'. FPR is the proportion of answers that were provided incorrectly as 'true'."
+      isCorrect: true
+      explanation: "Correct."
+    - content: "TPR is the proportion of answers that were provided correctly as 'true'. FPR is the proportion of answers that were provided incorrectly as 'false'."
+      isCorrect: false
+      explanation: "Incorrect."
+  - content: 'What are on the X and Y axes in an ROC plot?'
+    choices:
+    - content: 'X-axis: FP rate, Y-axis: TP rate'
+      isCorrect: true
+      explanation: "Correct."
+    - content: "X-axis: Number of FPs, Y-axis: Number of TPs"
+      isCorrect: false
+      explanation: "Incorrect. "
+    - content: "X-axis: Number of TPs, Y-axis: Number of FPs"
+      isCorrect: false
+      explanation: "Incorrect."
+  - content: 'What does area under the curve for an ROC plot tell us?'
+    choices:
+    - content: "How well the model works at its optimum decision threshold"
+      isCorrect: false
+      explanation: "Incorrect. This information might be obtainable from the ROC plot but we can't get this information from the AUC."
+    - content: "Which is the optimum decision threshold?"
+      isCorrect: false
+      explanation: "Incorrect. AUC is a summary metric that is too simplified to provide this information."
+    - content: "It gives a summary of how well a model works across various thresholds."
+      isCorrect: true
+      explanation: "Correct."
+
diff --git a/learn-pr/azure/optimize-model-performance-roc-auc/9-summary.yml b/learn-pr/azure/optimize-model-performance-roc-auc/9-summary.yml
@@ -1,13 +1,13 @@
-### YamlMime:ModuleUnit
-uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.summary
-title: Summary
-metadata:
-  title: Summary
-  description: An overview of the content covered in the module.
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: unit
-durationInMinutes: 3
-content: |
-  [!include[](includes/9-summary.md)] 
+### YamlMime:ModuleUnit
+uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.summary
+title: Summary
+metadata:
+  title: Summary
+  description: An overview of the content covered in the module.
+  ms.date: 04/03/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: unit
+durationInMinutes: 3
+content: |
+  [!include[](includes/9-summary.md)] 
diff --git a/learn-pr/azure/optimize-model-performance-roc-auc/index.yml b/learn-pr/azure/optimize-model-performance-roc-auc/index.yml
@@ -1,43 +1,43 @@
-### YamlMime:Module
-uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout
-metadata:
-  title: Measure and optimize model performance with ROC and AUC
-  description: Receiver operator characteristic curves are a powerful way to assess and fine-tune trained classification models. We introduce and explain the utility of these curves through learning content and practical exercises.
-  ms.date: 07/20/2024
-  author: s-polly
-  ms.author: scottpolly
-  ms.topic: module
-  ms.service: azure-machine-learning
-  ms.collection:
-    - TBD
-    - ce-skilling-ai-copilot
-title: Measure and optimize model performance with ROC and AUC
-summary: Receiver operator characteristic curves are a powerful way to assess and fine-tune trained classification models. We introduce and explain the utility of these curves through learning content and practical exercises.
-abstract: | 
-  In this module, you will:
-  - Understand how to create ROC curves.
-  - Explore how to assess and compare models using these curves.
-  - Practice fine-tuning a model using characteristics plotted on ROC curves.
-prerequisites: Familiarity with machine learning models
-iconUrl: /training/achievements/machine-learning/optimize-model-performance-roc-auc.svg
-levels: 
-- beginner
-roles: 
-- ai-engineer
-- data-scientist
-- student
-products: 
-- azure
-subjects:
-- classification-analysis
-units:
-- learn.machinelearning.optimize-model-performance-roc-auc-dropout.introduction
-- learn.machinelearning.optimize-model-performance-roc-auc-dropout.receiver-operator-characteristic-curve
-- learn.machinelearning.optimize-model-performance-roc-auc-dropout.exercise-evaluate-roc-curves
-- learn.machinelearning.optimize-model-performance-roc-auc-dropout.comparing-optimizing-curves
-- learn.machinelearning.optimize-model-performance-roc-auc-dropout.exercise-tune-auc-curves
-- learn.machinelearning.optimize-model-performance-roc-auc-dropout.knowledge-check
-- learn.machinelearning.optimize-model-performance-roc-auc-dropout.summary
-badge: 
-  uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.badge #add the uid for your achievement badge.
-
+### YamlMime:Module
+uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout
+metadata:
+  title: Measure and Optimize Model Performance with ROC and AUC
+  description: Receiver operator characteristic curves are a powerful way to assess and fine-tune trained classification models. We introduce and explain the utility of these curves through learning content and practical exercises.
+  ms.date: 04/03/2025
+  author: s-polly
+  ms.author: scottpolly
+  ms.topic: module
+  ms.service: azure-machine-learning
+  ms.collection:
+    - TBD
+    - ce-skilling-ai-copilot
+title: Measure and optimize model performance with ROC and AUC
+summary: Receiver operator characteristic curves are a powerful way to assess and fine-tune trained classification models. We introduce and explain the utility of these curves through learning content and practical exercises.
+abstract: | 
+  In this module, you will:
+  - Understand how to create ROC curves.
+  - Explore how to assess and compare models using these curves.
+  - Practice fine-tuning a model using characteristics plotted on ROC curves.
+prerequisites: Familiarity with machine learning models
+iconUrl: /training/achievements/machine-learning/optimize-model-performance-roc-auc.svg
+levels: 
+- beginner
+roles: 
+- ai-engineer
+- data-scientist
+- student
+products: 
+- azure
+subjects:
+- classification-analysis
+units:
+- learn.machinelearning.optimize-model-performance-roc-auc-dropout.introduction
+- learn.machinelearning.optimize-model-performance-roc-auc-dropout.receiver-operator-characteristic-curve
+- learn.machinelearning.optimize-model-performance-roc-auc-dropout.exercise-evaluate-roc-curves
+- learn.machinelearning.optimize-model-performance-roc-auc-dropout.comparing-optimizing-curves
+- learn.machinelearning.optimize-model-performance-roc-auc-dropout.exercise-tune-auc-curves
+- learn.machinelearning.optimize-model-performance-roc-auc-dropout.knowledge-check
+- learn.machinelearning.optimize-model-performance-roc-auc-dropout.summary
+badge: 
+  uid: learn.machinelearning.optimize-model-performance-roc-auc-dropout.badge #add the uid for your achievement badge.
+
